{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import tensorflow as tf"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "in2 = tf.keras.layers.Input(shape=(100,))\r\n",
    "in2"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor 'input_1:0' shape=(None, 100) dtype=float32>"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "in1 = tf.keras.layers.Input(shape=(100))\r\n",
    "in1"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor 'input_2:0' shape=(None, 100) dtype=float32>"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "in3 = tf.keras.layers.Input(shape=(100))\r\n",
    "in3"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor 'input_3:0' shape=(None, 100) dtype=float32>"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
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   "name": "python",
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   "codemirror_mode": {
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  "kernelspec": {
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  "interpreter": {
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